# Generating Active Explicable Plans in Human-Robot Teaming

arXiv: https://arxiv.org/abs/2109.08834 · HTML: https://mlsanity.com/paper/2109.08834/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior](https://mlsanity.com/paper/1811.09722/) (2018)
- [Plan Explicability and Predictability for Robot Task Planning](https://mlsanity.com/paper/1511.08158/) (2015)
- [A Unified Framework for Planning in Adversarial and Cooperative Environments](https://mlsanity.com/paper/1802.06137/) (2018)
- [AI Challenges in Human-Robot Cognitive Teaming](https://mlsanity.com/paper/1707.04775/) (2017)
- [Interactive Plan Explicability in Human-Robot Teaming](https://mlsanity.com/paper/1901.05642/) (2019)
- [A Bayesian Account of Measures of Interpretability in Human-AI Interaction](https://mlsanity.com/paper/2011.10920/) (2020)
- [Explicablility as Minimizing Distance from Expected Behavior](https://mlsanity.com/paper/1611.05497/) (2016)

## Built on by (most cited first, 2 of 2)

- [Safe Explicable Planning](https://mlsanity.com/paper/2304.03773/) (2023)
- [Safe Explicable Policy Search](https://mlsanity.com/paper/2503.07848/) (2025)

## Abstract (arXiv)

Intelligent robots are redefining a multitude of critical domains but are still far from being fully capable of assisting human peers in day-to-day tasks. An important requirement of collaboration is for each teammate to maintain and respect an understanding of the others' expectations of itself. Lack of which may lead to serious issues such as loose coordination between teammates, reduced situation awareness, and ultimately teaming failures. Hence, it is important for robots to behave explicably by meeting the human's expectations. One of the challenges here is that the expectations of the human are often hidden and can change dynamically as the human interacts with the robot. However, existing approaches to generating explicable plans often assume that the human's expectations are known and static. In this paper, we propose the idea of active explicable planning to relax this assumption. We apply a Bayesian approach to model and predict dynamic human belief and expectations to make explicable planning more anticipatory. We hypothesize that active explicable plans can be more efficient and explicable at the same time, when compared to explicable plans generated by the existing methods. In our experimental evaluation, we verify that our approach generates more efficient explicable plans while successfully capturing the dynamic belief change of the human teammate.